| 摘要: |
| 电力负荷预测的精确度对于电厂的实际发电量、配电、系统维护以及与电价相关的能源供应商运营计划等都有着极大地影响;研究了前馈深度神经网络和递归深度神经网络在中期电力负荷预测中的应用及其准确性和计算能力分析;首先，针对收集的原始数据集进行预处理，提出了一种时域-频域分析特征提取方法，该方法可以充分地挖掘隐藏在原始数据集中的深层信息；然后利用前馈深度神经网络和递归深度神经网络模型进行中期电力负荷预测；最后，利用某城市5年期间的实际负荷数据，预测未来1年中不同季节的负荷；通过仿真结果表明：时域-频域分析法和深度神经网络协同使用于中期负荷预测具有更高的准确性。 |
| 关键词: 中期负荷预测 前馈深度神经网络 递归深度神经网络 时域-频域分析 |
| DOI： |
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| Medium Term Power Load Forecasting Using Deep Neural Networks |
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WANG Jun
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| Abstract: |
| Accurate load forecasting greatly influences the planning processes undertaken in operation centers of energy providers that relate to the actual electricity generation，distribution，system maintenance as well as electricity pricing This paper exploits the applicability and compares the performance of the FF-DNN and R-DNN models on the basis of accuracy and computational performance in the medium term forecast of electricity load Firstly，a time frequency feature selection procedure is proposed because the introduced scheme may adequately learn hidden patterns Then，the FF-DNN and R-DNN models are used for the medium term load forecasting Finally，the herein proposed method is used to predict the load in different seasons in the coming year over real datasets gathered in a period of 5 years Overall，our generated outcomes reveal that the synergistic use of TF feature analysis with DNN enables to obtain higher accuracy |
| Key words: medium term load forecasting(MTLF) feed forward deep neural network ( FF-DNN) recurrent deep neural network(R-DNN) time frequency analysis(TF) |
